SnRNA-seq

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snRNA-seq, also known as single nucleus RNA sequencing, single nuclei RNA sequencing or sNuc-seq, is an RNA sequencing method for profiling gene expression in cells which are difficult to isolate, such as those from tissues that are archived or which are hard to be dissociated. It is an alternative to single cell RNA seq (scRNA-seq), as it analyzes nuclei instead of intact cells.

Contents

snRNA-seq minimizes the occurrence of spurious gene expression, as the localization of fully mature ribosomes to the cytoplasm means that any mRNAs of transcription factors that are expressed after the dissociation process cannot be translated, and thus their downstream targets cannot be transcribed. [1] Additionally, snRNA-seq technology enables the discovery of new cell types which would otherwise be difficult to isolate.

Methods and technology

The basic snRNA-seq method requires 4 main steps: tissue processing, nuclei isolation, cell sorting, and sequencing. In order to isolate and sequence RNA inside the nucleus, snRNA-seq involves using a quick and mild nuclear dissociation protocol. This protocol allows for minimization of technical issues that can affect studies, especially those concerned with immediate early gene (IEG) behavior.

The resulting dissociated cells are suspended and the suspension gently lysed, allowing the cell nuclei to be separated from their cytoplasmic lysates using centrifugation. [2] These separated nuclei/cells are sorted using fluorescence-activated cell sorting (FACS) into individual wells, and amplified using microfluidics machinery. [2] Sequencing occurs as normal and the data can be analyzed as appropriate for its use. This basic snRNA-seq methodology is capable of profiling RNA from tissues that are preserved or cannot be dissociated, but it does not have high throughput capability due to its reliance on nuclei sorting by FACS. [3] This technique cannot be scaled easily to profiling large numbers of nuclei or samples. Massively parallel scRNA-seq methods exist and can be readily scaled but their requirement of a single cell suspension as input is not ideal and eliminates some of the flexibility that is available with the snRNA-seq method in regards to the types of tissues and cells that can be examined. [3] In response, the DroNc-Seq method of massively parallel snRNA-seq with droplet technology was developed by researchers from the Broad Institute of MIT and Harvard. [3] In this technique, nuclei that have been isolated from their fixed or frozen tissue are encapsulated in droplets with uniquely barcoded beads that are coated with oligonucleotides containing a 30-terminal deoxythymine (dT) stretch. [4] This coating captures the polyadenylated mRNA content produced when the nuclei are lysed inside the droplets. [4] The captured mRNA is reverse transcribed into cDNA after emulsion breakage. [4] Sequencing this cDNA produces the transcriptomes of all the single nuclei being looked at and these can be used for many purposes, including identification of unique cell types. [5]

The sequencing tools and equipment used in scRNA-seq can be used with modifications for snRNA-seq experiments. Illumina outlines a workflow for the basic snRNA-seq method which can be performed with existing equipment. [2] DroNc-Seq can be accomplished with microfluidic platforms which are meant for the Drop-seq scRNA-seq method. However, Dolomite Bio has adapted one of their instruments, the automated Nadia platform for scRNA-seq, to be used natively for DroNc-Seq as well. [5] This instrument could simplify the generation of single nuclei sequencing libraries, as it is being used for its intended purpose.

In regard to data analysis after sequencing, a computational pipeline known as dropSeqPipe was developed by the McCarroll Lab at Harvard. [6] Although the pipeline was originally developed for use with Drop-seq scRNA-seq data, it can be used with DroNc-Seq data as it also utilizes droplet technology.

Difference between snRNA-seq and scRNA-seq

snRNA-seq uses isolated nuclei instead of the entire cells to profile gene expression. That is to say, scRNA-seq measures both cytoplasmic and nuclear transcripts, while snRNA-seq mainly measures nuclear transcripts (though some transcripts might be attached to the rough endoplasmic reticulum and partially preserved in nuclear preps). [7] This allows for snRNA-seq to process only the nucleus and not the entire cell. For this reason, compared to scRNA-seq, snRNA-Seq is more appropriate to profile gene expression in cells that are difficult to isolate (e.g. adipocytes, neurons), as well as preserved tissues. [5]

Additionally, the nuclei required for snRNA-seq can be obtained quickly and easily from fresh, lightly fixed, or frozen tissues, whereas isolating single cells for single-cell RNA-seq (scRNA-seq) involves extended incubations and processing. This gives researchers the ability to obtain transcriptomes which are not as perturbed during isolation.

Application

In neuroscience, neurons have an interconnected nature which makes it extremely hard to isolate intact single neurons. [8] As snRNA-seq has emerged as an alternative method of assessing a cell's transcriptome through the isolation of single nuclei, it has been possible to conduct single-neuron studies from postmortem human brain tissue. [9] snRNA-seq has also enabled the first single neuron analysis of immediate early gene expression (IEGs) associated with memory formation in the mouse hippocampus. [1] In 2019, Dmitry et al used the method on cortical tissue from ASD patients to identify ASD-associated transcriptomic changes in specific cell types, which is the first cell-type-specific transcriptome assessment in brains affected by ASD. [10]

Outside of neuroscience, snRNA-seq has also been used in other research areas. In 2019, Haojia et al compared both scRNA-seq and snRNA-seq in a genomic study around the kidney. They found snRNA-seq accomplishes an equivalent gene detection rate to that of scRNA-seq in adult kidney with several significant advantages (including compatibility with frozen samples, reduced dissociation bias and so on ). [11] In 2019, Joshi et al used snRNA-seq in a human lung biology study in which they found snRNA-seq allowed unbiased identification of cell types from frozen healthy and fibrotic lung tissues. [12] Adult mammalian heart tissue can be extremely hard to dissociate without damaging cells, which does not allow for easy sequencing of the tissue. However, in 2020, German scientists presented the first report of sequencing an adult mammalian heart by using snRNA-seq and were able to provide practical cell‐type distributions within the heart [13]

Pros and cons of snRNA-seq

Pros

  1. In scRNA-seq, the dissociation process may impair some sensitive cells and some cells in certain tissues (e.g. collagenous matrix [11] ) can be extremely hard to dissociate. Such issues can be prevented in snRNA-seq as we only need to isolate a single nucleus instead of an entire single cell.
  2. Unlike scRNA-seq, snRNA-seq has quick and mild nuclei dissociation protocols that would forestall technical issues emerging from heating, protease digestion. [2]
  3. snRNA-seq works very well for preserved/frozen tissues. [5]

Cons

  1. Sequencing RNA in the cytoplasm (gene isoforms, RNA in mitochondria and chloroplast etc.) is not possible, as snRNA-seq mostly measures nuclear transcripts.

Related Research Articles

<span class="mw-page-title-main">Complementary DNA</span> Single-stranded DNA synthesized from RNA

In genetics, complementary DNA (cDNA) is DNA synthesized from a single-stranded RNA template in a reaction catalyzed by the enzyme reverse transcriptase. cDNA is often used to express a specific protein in a cell that does not normally express that protein, or to sequence or quantify mRNA molecules using DNA based methods. cDNA that codes for a specific protein can be transferred to a recipient cell for expression, often bacterial or yeast expression systems. cDNA is also generated to analyze transcriptomic profiles in bulk tissue, single cells, or single nuclei in assays such as microarrays, qPCR, and RNA-seq.

The transcriptome is the set of all RNA transcripts, including coding and non-coding, in an individual or a population of cells. The term can also sometimes be used to refer to all RNAs, or just mRNA, depending on the particular experiment. The term transcriptome is a portmanteau of the words transcript and genome; it is associated with the process of transcript production during the biological process of transcription.

<span class="mw-page-title-main">RNA-Seq</span> Lab technique in cellular biology

RNA-Seq is a sequencing technique that uses next-generation sequencing (NGS) to reveal the presence and quantity of RNA in a biological sample, representing an aggregated snapshot of the cells' dynamic pool of RNAs, also known as transcriptome.

Gene isoforms are mRNAs that are produced from the same locus but are different in their transcription start sites (TSSs), protein coding DNA sequences (CDSs) and/or untranslated regions (UTRs), potentially altering gene function.

<span class="mw-page-title-main">Single-cell analysis</span> Testbg biochemical processes and reactions in an individual cell

In the field of cellular biology, single-cell analysis and subcellular analysis is the study of genomics, transcriptomics, proteomics, metabolomics and cell–cell interactions at the single cell level. The concept of single-cell analysis originated in the 1970s. Before the discovery of heterogeneity, single-cell analysis mainly referred to the analysis or manipulation of an individual cell in a bulk population of cells at a particular condition using optical or electronic microscope. To date, due to the heterogeneity seen in both eukaryotic and prokaryotic cell populations, analyzing a single cell makes it possible to discover mechanisms not seen when studying a bulk population of cells. Technologies such as fluorescence-activated cell sorting (FACS) allow the precise isolation of selected single cells from complex samples, while high throughput single cell partitioning technologies, enable the simultaneous molecular analysis of hundreds or thousands of single unsorted cells; this is particularly useful for the analysis of transcriptome variation in genotypically identical cells, allowing the definition of otherwise undetectable cell subtypes. The development of new technologies is increasing our ability to analyze the genome and transcriptome of single cells, as well as to quantify their proteome and metabolome. Mass spectrometry techniques have become important analytical tools for proteomic and metabolomic analysis of single cells. Recent advances have enabled quantifying thousands of protein across hundreds of single cells, and thus make possible new types of analysis. In situ sequencing and fluorescence in situ hybridization (FISH) do not require that cells be isolated and are increasingly being used for analysis of tissues.

Single-cell sequencing examines the nucleic acid sequence information from individual cells with optimized next-generation sequencing technologies, providing a higher resolution of cellular differences and a better understanding of the function of an individual cell in the context of its microenvironment. For example, in cancer, sequencing the DNA of individual cells can give information about mutations carried by small populations of cells. In development, sequencing the RNAs expressed by individual cells can give insight into the existence and behavior of different cell types. In microbial systems, a population of the same species can appear genetically clonal. Still, single-cell sequencing of RNA or epigenetic modifications can reveal cell-to-cell variability that may help populations rapidly adapt to survive in changing environments.

<span class="mw-page-title-main">Transcriptome in vivo analysis tag</span>

A transcriptome in vivo analysis tag is a multifunctional, photoactivatable mRNA-capture molecule designed for isolating mRNA from a single cell in complex tissues.

<span class="mw-page-title-main">Epitranscriptomic sequencing</span>

In epitranscriptomic sequencing, most methods focus on either (1) enrichment and purification of the modified RNA molecules before running on the RNA sequencer, or (2) improving or modifying bioinformatics analysis pipelines to call the modification peaks. Most methods have been adapted and optimized for mRNA molecules, except for modified bisulfite sequencing for profiling 5-methylcytidine which was optimized for tRNAs and rRNAs.

Perturb-seq refers to a high-throughput method of performing single cell RNA sequencing (scRNA-seq) on pooled genetic perturbation screens. Perturb-seq combines multiplexed CRISPR mediated gene inactivations with single cell RNA sequencing to assess comprehensive gene expression phenotypes for each perturbation. Inferring a gene’s function by applying genetic perturbations to knock down or knock out a gene and studying the resulting phenotype is known as reverse genetics. Perturb-seq is a reverse genetics approach that allows for the investigation of phenotypes at the level of the transcriptome, to elucidate gene functions in many cells, in a massively parallel fashion.

Single-cell transcriptomics examines the gene expression level of individual cells in a given population by simultaneously measuring the RNA concentration of hundreds to thousands of genes. Single-cell transcriptomics makes it possible to unravel heterogeneous cell populations, reconstruct cellular developmental pathways, and model transcriptional dynamics — all previously masked in bulk RNA sequencing.

Transcriptomics technologies are the techniques used to study an organism's transcriptome, the sum of all of its RNA transcripts. The information content of an organism is recorded in the DNA of its genome and expressed through transcription. Here, mRNA serves as a transient intermediary molecule in the information network, whilst non-coding RNAs perform additional diverse functions. A transcriptome captures a snapshot in time of the total transcripts present in a cell. Transcriptomics technologies provide a broad account of which cellular processes are active and which are dormant. A major challenge in molecular biology is to understand how a single genome gives rise to a variety of cells. Another is how gene expression is regulated.

<span class="mw-page-title-main">Spatial transcriptomics</span> Range of methods designed for assigning cell types

Spatial transcriptomics is a method for assigning cell types to their locations in the histological sections. This method can also be used to determine subcellular localization of mRNA molecules. The term is a variation of Spatial Genomics, first described by Doyle, et al., in 2000 and then expanded upon by Ståhl et al. in a technique developed in 2016, which has since undergone a variety of improvements and modifications.

CITE-Seq is a method for performing RNA sequencing along with gaining quantitative and qualitative information on surface proteins with available antibodies on a single cell level. So far, the method has been demonstrated to work with only a few proteins per cell. As such, it provides an additional layer of information for the same cell by combining both proteomics and transcriptomics data. For phenotyping, this method has been shown to be as accurate as flow cytometry by the groups that developed it. It is currently one of the main methods, along with REAP-Seq, to evaluate both gene expression and protein levels simultaneously in different species.

<span class="mw-page-title-main">Patch-sequencing</span>

Patch-sequencing (patch-seq) is a method designed for tackling specific problems involved in characterizing neurons. As neural tissues are one of the most transcriptomically diverse populations of cells, classifying neurons into cell types in order to understand the circuits they form is a major challenge for neuroscientists. Combining classical classification methods with single cell RNA-sequencing post-hoc has proved to be difficult and slow. By combining multiple data modalities such as electrophysiology, sequencing and microscopy, Patch-seq allows for neurons to be characterized in multiple ways simultaneously. It currently suffers from low throughput relative to other sequencing methods mainly due to the manual labor involved in achieving a successful patch-clamp recording on a neuron. Investigations are currently underway to automate patch-clamp technology which will improve the throughput of patch-seq as well.

<span class="mw-page-title-main">RNA timestamp</span>

An RNA timestamp is a technology that enables the age of any given RNA transcript to be inferred by exploiting RNA editing. In this technique, the RNA of interest is tagged to an adenosine rich reporter motif that consists of multiple MS2 binding sites. These MS2 binding sites recruit a complex composed of ADAR2 and MCP. The binding of the ADAR2 enzyme to the RNA timestamp initiates the gradual conversion of adenosine to inosine molecules. Over time, these edits accumulate and are then read through RNA-seq. This technology allows us to glean cell-type specific temporal information associated with RNA-seq data, that until now, has not been accessible.

Deterministic Barcoding in Tissue for Spatial Omics Sequencing (DBiT-seq) was developed at Yale University by Rong Fan and colleagues in 2020 to create a multi-omics approach for studying spatial gene expression heterogenicity within a tissue sample. This method can used for the co-mapping mRNA and protein levels at a near single-cell resolution in fresh or frozen formaldehyde-fixed tissue samples. DBiT-seq utilizes next generation sequencing (NGS) and microfluidics. This method allows for simultaneous spatial transcriptomic and proteomic analysis of a tissue sample. DBiT-seq improves upon previous spatial transcriptomics applications such as High-Definition Spatial Transcriptomics (HDST) and Slide-seq by increasing the number of detectable genes per pixel, increased cellular resolution, and ease of implementation.

Single-cell genome and epigenome by transposases sequencing (scGET-seq) is a DNA sequencing method for profiling open and closed chromatin. In contrast to single-cell assay for transposase-accessible chromatin with sequencing (scATAC-seq), which only targets active euchromatin. scGET-seq is also capable of probing inactive heterochromatin.

VINE-seq is a method to isolate and molecularly characterize the vascular and perivascular cells of the human brain microvessels at single-nuclei resolution. This technique is achieved by combining various known laboratory-based strategies involving the mechanical dissociation of brain tissue samples into single cells, density gradient centrifugation and filtration to isolate nuclei of microvessels, fluorescence-activated cell sorting (FACs) of cellular populations and droplet-based single-nuclei RNA sequencing (drop-snRNA-seq). Altogether, this generates a single-nuclei transcriptomic profile of the various cell types present in the vasculature of the brain. Through processing and analyzing the single-nuclei transcriptomic data, the heterogeneity within and between cell types can be distinguished to construct the molecular landscape of the human brain vasculature that was not previously done before.

References

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  10. ↑ Velmeshev, Dmitry; Schirmer, Lucas; Jung, Diane; Haeussler, Maximilian; Perez, Yonatan; Mayer, Simone; Bhaduri, Aparna; Goyal, Nitasha; Rowitch, David H.; Kriegstein, Arnold R. (2019-05-17). "Single-cell genomics identifies cell type–specific molecular changes in autism". Science. 364 (6441): 685–689. Bibcode:2019Sci...364..685V. doi: 10.1126/science.aav8130 . ISSN   0036-8075. PMC   7678724 . PMID   31097668. S2CID   156055368.
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